However, most of the previous methods use only the surface information in texts.

they ignore the latent information in texts, which could assist in improving IR performance.


Then, for each document containing q in the collection, the KL-divergence between the DIR-based document model and the MLE-based query model is computed.

the score of a document d for a query q is defined as follows.


This interpolation is based on Zhai and Lafferty's linear combination method (see Section 2.3).

then, for each document d i in the search results D q , we compute the KL-divergence between the score of document d i for query q and feedback F is defined as re-ranking score( Finally, we obtain the revised search results by re-ranking the documents in D q according to their re-ranking scores.


All data used in our experiments are taken from PTB and PDTB.

folders 02 − 22 are used to train the model, while folders 00 − 01 belong to the development set, and folders 23 and 24 are meant for testing.


As machine learning methods scale up and now deal with millions of features, we ideally want to add all possible features without having to manually verify or consider the effectiveness of each feature with respect to the performance.

we need an automatic way to exploit any usable information that can be obtained from features.


On the other hand, our α is estimated given a noisy relation among features, thus it plays a different role from w. In this sense, the way to handle this additional parameter for relational lasso is completely different from adaptive lasso.

the originality of our method lies in using α to express the relations between underlying features.


The relations between features are denoted as R, which is provided to the proposed algorithm and used to estimate α. R denotes a pairwise dependence relation between features.

if there are m features in total, 1 Oracle property (Fan and Li , 2001) is satisfied if the optimization problem can correctly select the nonzero weights with probability converging to one and the estimators of the nonzero weights are asymptotically normal with the same means and covariance that they would have if the zero coefficients were known in advance.


In the second stage, these features will never re-acquire any importance.

the feature selection must be done at the very end in order to preserve the possibility of some features to re-acqure the importance through learning.


On the aspect of classification accuracy, MetaTD catches up with the 1-vs-Rest approach.

metaTD slightly outperforms 1-vs-Rest approach on both data sets when the number of classes exceeds 5 thousands.


Therefore, TO was assigned to cases whose ratio of "Simulation accuracy of theOther / Simulation accuracy of Target Only" was lower than 0.40.

tO was assigned to the cases when the accuracy of WSD when a classifier was trained with ten labeled word tokens of the target data and tested using a leaveone-out cross-validation method was higher than the accuracy of WSD when a classifier was trained with the source data and tested using ten labeled word tokens of the target data.


That is, TO was assigned to the cases when the accuracy of WSD when a classifier was trained with ten labeled word tokens of the target data and tested using a leaveone-out cross-validation method was higher than the accuracy of WSD when a classifier was trained with the source data and tested using ten labeled word tokens of the target data.

this indicates that the simulation using ten manually labeled word tokens of the target data was an important clue in predicting the optimal DA method.


Therefore, in addition to the ranking scores above, during the selection process we consider more constraints.

for each instance we compare the labels generated based on three sources: original automatically derived label, prediction from the current classifier, and prediction from the initial self-trained classifier.


We represent the meaning of any word w ∈ W by a vector in the vector space V spanned by the set of basis vectors {e (r,w ) | r ∈ R, w ∈ W }.

such a vector records the association strength between w and any context word w occurring in relation r. we associate a word w ∈ W with a vector where f is a function that assigns a weight to the dependency triple (w, r, w ).


It shows how often the k first candidates in the ranking contain at least one gold standard paraphrase.

we can observe that similarity-based contextualization predicts a good top-ranked candidate in 55% of the cases; the top three contain a correct paraphrases in more than 80% of the cases.


As can be seen from Table 1, a significant performance gain was observed for both subjLDA and LDA over the baseline.

more than 8% gain was observed for subjLDA, giving the best overall accuracy of 71.2% which is 3.1% and 3.6% higher than LDA(Sent.)


It seems that we can solve the problem of extracting resource terms using a simple graph propagation strategy.

given an application domain corpus, the user first provides a few seed resource terms.


The resulting values are used to initialize the system for discovering resource terms from each application domain.

we do not need to execute stage 1 anymore.


It is also important to note that those resources that have been used as global seeds in stage 1 of our algorithm are not counted in the precision computation for the results in the tables.

the discovered resource terms are all new.


As one may have noticed, without any further constraints on the concept of contexts, the top 3 bound morphemes with the highest contextual diversity will be '-d', '-e' and '-t', which do not comply with our understanding of functional morphemes in English.

for acquiring inflectional or derivational suffix/prefixes, we want to compute contextual diversity of bound morphemes according to properly justified contextual relations only, instead of from all arbitrary divisions of words.


We are not aware of previous work that acquires closed-class words in an automatic process, and in the NLP field, they are often approximated by the most frequent words.

as a baseline model, we can acquire closed-class words by selecting k top words of the highest diversity measured by tokenC.


The addition of the tagging features requires a few new elements to be added into the set of kernel features; the new set of kernel features for Joint-HS + is shown in  Since our joint framework performs tagging and parsing simultaneously, we can think of incorporating a combined feature of the next tag (to be assigned to q 0 ) with syntactic information from stack elements, which cannot be used in an n-gram POS tagger.

we propose to use the features shown in Table 3 (b).


In French, there are two resources of adverbs that follow different principles both in classification and in representation within the Lexicon-Grammar framework.

firstly, tables of simple adverbs ending in -ment "-ly" (Moliner, 1984;Molinier and Lévrier, 2000), which are mainly derived from adjectives and, secondly, tables of compound adverbs (Gross, 1986;.


Each cell corresponds to the validity of a feature in a class.

the Three major classes of sentential adverbs are worth mentioning: conjuncts, style disjuncts (or utterance-level adverbs) and attitude disjuncts (or statement adverbs).


This criterion ensures a complementarity between lexicon and grammar.

it tends to ensure that any combination of linguistic elements which is correct in the language, but is not represented in common syntactic-semantic grammars, should be stored in lexicons.


Semantically, by definition, compound adverbs cannot be decomposed into simple units.

the resulting overall meaning of the compound adverb cannot usually be deduced from the sum of the meaning of its component elements.


While we propose a general approach to analyzing syntactic representations, throughout the paper we will use examples based on converting dependency structures to phrase structures.

we will use as a source of examples the Hindi/Urdu Treebank (HUTB) .


When a linguistic phenomenon (e.g., argument sharing in an embedded small clause) is represented in both DS and PS but in different ways, the automatic DS-to-PS conversion is still possible if we can automatically create a new DS, let us call it DS+, which is derived from the original DS but is consistent with the PS.

dS+ and PS represent that phenomenon in the same way.


In this case, a list of unergative and unaccusative verbs in Hindi can provide this information, since all instances of a particular intransitive verb are always either unergative or unaccusative.

automatic DS-to-PS conversion is impossible unless an additional resource is provided that allows the conversion mechanism to make the unaccusative/unergative distinction.


We have then provided a procedure for comparing treebanks guidelines with respect to conversion.

we have argued that the conversion from one treebank to another must be examined on a phenomenon-by-phenomenon basis, and that for each phenomenon, there are three scenarios that may arise: the two guidelines have compatible analyses; they have incompatible analyses; and one represents the phenomenon but the other does not.


Certainly, dependency representation has drawbacks of its own in terms of ambiguity, some of which are specific to dependency representation.

figure 1 shows a construction with an unambiguous constituent structure, which in dependency space is ambiguous with respect to the attachment of the adverb [Daum et al., 2004].


Description of training, development and test data are reported in table 1 and 2.

the test set is constituted by different kind of data: transcriptions of broadcast news, and broadcast conversations.


Its main characteristics are the possibility to include a huge number of features, like the Maximum Entropy model, but computing global conditional probabilities normalized at sentence level, instead of position level.

this last point results very effective since it solves the label bias problem, as pointed out in (Lafferty et al., 2001).


Given the two annotation steps implemented with CRF and PCFG, our system is in principle equivalent to a single system for syntactic parsing performing a "one-shot" annotation.

solutions like (Charniak and Johnson, 2005) and (Collins and Koo, 2005) use POS tags as tree leaves.


We show that one of the ways in which clausal parsing helps is by better learning of features that leads to improved label accuracy for Hindi.

we show that ambiguous case markers (or suffixes) that appear with many relations can be disambiguated successfully.


We now define the input to the 2 nd stage for 2-Hard more precisely, Let T be the complete tree that should be output by the 2 nd stage parser and let G be the subgraph of T that is input to the second stage.

then G should satisfy the following constraint: if the arc x → y is in G, then, for every z such that y → z is in t, y → z is also in G. if an arc is included in the 1 st stage partial parse, the complete subtree under the dependent must also be included.


Also, Malt suffers at long distance dependencies because of local feature optimization (McDonald and Nivre, 2007).

for Malt, depth and errors are negatively correlated while arc-distance and errors are positively correlated.


The arising M-step decomposes into several independent optimization problems.

there is a separate problem for each e 1 , e 2 to update the respective dictionary distribution.


We solve this via gradient descent with the gradient In special cases more efficient schemes are applicable.

it is well-known that if w f e 1 ,e 2 = 0 for all f the optimal solution is given by re-normalizing the coefficients c f e 1 ,e 2 .


We extend the traditional HMM to allow a broader range of phone mapping configurations.

the null transition (Bahl et al., 1982) is used to represent skipping a state without consuming any observations.


The point being made here is that handling these differences is crucial to the correct meaning being conveyed.

simply translating non-finite clauses separately with reordering constraints around them, will not lead to good translation, because the translation of these clauses is often dependent on the superordinate clause, and also there is reordering between these clauses and the superordinate clause.


4 and allow it to adapt to the hypothesis space of a set of given systems.

we write a loss function L(e ′ |e; θ), parameterized by θ, as a linear combination of sub-components: The sub-components are related to the original loss function in some fashion.


We experiment with the parallel Chinese-Spanish corpus (United Nations) to explore alternatives of SMT strategies which consist on using a pivot language.

two well-known alternatives are shown for pivoting: the cascade system and the pseudo-corpus.


Using most of the languages available in the UN parallel corpora (English, Spanish, Chinese, Arabic and French) we built and compare several translation systems in order to study the impact of the different pivot languages when translating from Chinese to Spanish.

we built seven Chinese-Spanish systems: the direct Chinese-Spanish system as a quality upper bound; three cascade approach and three pseudo-corpus, using English, Arabic and French as pivots.


Additionally, the 5-gram language model is implemented as a back-off model.

if the frequency of a higher-order n-gram is zero, then its lower-order n-grams will be considered.


This approach performs SVD on a term-term co-occurrence matrix, which has been shown to outperform traditional LSA models that use term-document cooccurrence information.

a given word w is initially represented by a vector v of all its co-occurring words in a small collocation context (a 5-word window), i.e., v = (v 1 , .


2, removing pairs in the smallvalue range of the measure strongly decreases ESA (which, after half of the pairs are removed, becomes chaotic), while the same operation steadily increases WNP.

small-value pairs are crucial positive contributors for ESA, but rather negative contributors for WNP.


their effectiveness in Information Retrieval systems (Fox and Shaw, 1994).

we combine the text-based and image-based metrics by summing their relatedness figures (SUM) and by calculating their F-measure (F1) defined as the harmonic mean of the two input metrics.


There is consistent improvement for all the categories.

there is a relatively larger gain for the "feedback" class, which we expect may have a great impact on subsequent thread level classification.


The first issue we encountered is how to segment an article into arguments.

the issue concerns the determination of the argument boundaries.


Dataset Statistics Comparatively, the model using all the features achieves a precision of 60.22%.

these features are complementary for recognizing the Temporal relation.


One common characteristic of these two pairs are that the two POS tags play similar roles from syntactic view.

their syntactic contexts are usually similar, which naturally explains why the gold-standard syntactic features fail to help.


The corpus also serves as a repository for linguistic research.

it facilitates research in comparative grammar, by lending statistical evidence, and potentially demonstrating exceptions or other differences yet unnoticed.


With this, we eliminate the need to collect vast amounts of training data, thus aiding faster deployment for new domains.

our contributions are: • A semi-supervised answer extraction method for discussions: This paper makes the first attempt at extracting answers from discussions in a semisupervised manner.


Here, we would like to point out that acknowledgment is a strong indicator only when it is available.

even if we learn to classify acknowledgment posts perfectly, it cannot classify all answer posts perfectly since not all answers are acknowledged.


In this study, inspired by some multi-relational data mining techniques, such as (Ng et al., 2011), we assume each topic as a single relation type and construct an intra-topic word network for each relation type.

it is to map word relatedness within multiple topics to heterogeneous relations, meaning that words have interactions with others based on different topics.


In relation extraction, syntactic relatedness between the candidate entities of a relation is usually considered an important cue (Zhou et al., 2005;Mintz et al., 2009).

the specific type of syntactic relation needs to be considered.


Next, we detail how the replacement model is learned.

we first introduce how we automatically extract from search log data a set of labeled data with 'consistency' as a guidance and then explain how a 'replacement model' can be learned from this data set.


Both sentences are normalized to the predicate-argument structure (c).

the intransitive verb " " (rise) is a different word from the transitive verb " " (raise) but both are canonicalized to the same transitive verb " " (raise) in the predicate-argument structure.


The score of dependencies is further calculated as a weighted sum of the following two scores: the score of dependencies with consistent (matched) type and that with inconsistent (mismatched) type.

the score of dependencies with inconsistent type is reduced compared to the score of dependencies with consistent type.


We can see that our proposed method "word+pa" outperformed the baselines "word" and "word+dep" in all the metrics.

the difference between "word+dep" and "word+pa" in MAP was statistically significant with p = 0.01134.


These sentences are normalized to the predicate-argument structures (a') and (b'), respectively.

the intransitive verb " " (exist) is a different word from the transitive verb " " (put) but both are canonicalized to the same transitive verb " " (put) in the predicate-argument structures.


Experiments on the standard test collections of Japanese and English indicated the effectiveness of our approach.

the proposed method outperformed a baseline method that uses syntactic dependencies output by a dependency parser.


However, we can see that in DP-GT-BR, their impact was relatively small.

lower-level edges played more important roles in DP-GT-BR than in other models.


Although, the way case markers are realized may not affect the animacy prediction directly, it may impact the extraction of case marked distribution of nominals.

in case of agglutinative and inflectional languages extracting the multiple case marked word forms of a particular noun stem could be a challenging task.


We tested the feasibility of our approach on a set of problems selected from Japanese university entrance exams.

we wanted 1) to test the coverage of the semantic composition mechanism presented in §4 on real problems, and 2) to verify that there is no significant loss in the capability of the system due to the additional computational cost incurred by the separation of the semantic analysis from the mathematical reasoning.


The second point was confirmed by providing the ideal (100% correct) output from the (forthcoming) NLP components to a state-of-theart automatic reasoner and comparing the result against the performance of the reasoner on the input formulated by a human expert.

we manually gave the semantic representations of the problems as iDRSs or CCG derivation trees, and then automatically rewrote them into the language of RCF.


Therefore the identification of topics discussed in these channels could aid in different scenarios including violence detection and emergency response.

the task of classifying tweets as violence-related poses different challenges including: high topical diversity; irregular and ill-formed words; event-dependent vocabulary characterising violence-related content; and an evolving jargon emerging from violent events.


PMI is an automatic topic coherence evaluation which has been found to correspond well with human judgements on topic coherence.

a coherent topic should only contain semantically related words and hence any pair of the top words from the same topic should have a large PMI value.


This kind of spammers have an incredible high best answer rate, compared to normal users.

in a possible best answer spammer pair, sometimes only one user has an incredible high best answer rate.


In this work, we present a novel method for normalizing informal word to their formal equivalents.

given an informal word with its context as input, we generate hypotheses for its formal equivalents by searching the Google Web 1T corpus (Brants and Franz, 2006).


All entries that match at least one of the five rules are collected as formal candidates.


Choosing the feature in the center and the rightmost feature has a similar effect and at the same time avoids using the overly general feature that encompasses both as well as the sibling of the rightmost one, which is not an appropriate one.

we should select as general a feature as possible as long as none of them are considered irrelevant for the class, thereby it can cover the semantics of the features underneath it, without which we can achieve better efficiency.


While general feature selection methods such as χ 2 are based on the degree of belief, our selection method considers the reliability and applicability (or generality) of a feature.

a feature we choose should have a high lift value (i.e., high reliability) and lie closest to the root on a path so that we can broaden its applicability.


Our experimental results show that the proposed method is significantly effective in reducing the feature space compared to the wellknown feature selection methods, and yet the overall effectiveness is similar to or sometimes better than a state-of-the-art approach depending on the PoS of the events.

the effectiveness for noun events was improved quite meaningfully when the feature space was reduced significantly.


In this internet era, everyday people express opinions, comments, or sentiments; all of which can be accessed via the web.

with the popularization of mobile computing devices, it has become easier than ever for people to share messages using social media services like Twitter or Facebook.


4 Morphological analysis has difficulty when analyzing such phonetically written words since it makes distinctions based on Hangul syllables instead of the string of the letters.

'mes-iss-e' and 'me-si-sse' are discriminated because the hyphens are taken as the boundary of the syllables even though this is not the case during pronunciation.


Given the above issues, the present study revisits lattice reranking by exploring the effectiveness of the lattice generation algorithm.

large-scale experiments were conducted on three Japanese data sets.


Table 4 reveals that the reranking system using the pruning-based algorithm consumes the vast majority of the time for lattice generation.

the pruning-based algorithm is not efficient enough.


Basically we only improve the method for building a word lattice and do not change the process for finding an optimal path from the lattice.

our proposed system only adds new nodes to the word lattice built by the baseline system by exploiting the derivation rules and onomatopoeia patterns.


There are many onomatopoeias in Japanese.

there are a lot of unfamiliar onomatopoeias in Web text.


Note that while our system's recall denotes the ratio of actually recognized words, the coverage of UniDic word segmentation, especially for unknown words.


By introducing all the derivation rules and onomatopoeia patterns, there are 4,560 improvements for 100,000 sentences with only 80 deteriorations and a 6.2% loss in speed.

the derivation rules of insertion and substitution of long sound symbols and lowercases produced 3,327 improvements for 100,000 sentences at high recall values (see Table 4) with only 27 deteriorations and a 3.8% loss in speed.


Second, structure compilation (Liang et al., 2008) is employed to transfer the predictive power of hybrid models to sequence models via large-scale unlabeled data.

hybrid systems are utilized to create large-scale pseudo training data for cheap sequence models.


We tag giga-word sentences by applying the stacked first order LGLMs with all other models.

the HMMLA, PCFGLA and DEP systems are applied to tag unlabeled data features and their outputs are utilized to define features for first-order and second-order LGLMs which produce pseudo training data.


In this paper, we study two techniques to build accurate and fast sequence models for Chinese POS tagging.

our goal is to capture long-distance dependencies in sequence models.


• Case Ambiguity: Including the semantics from HWN to help disambiguate the con-fusion present in a case marker, has improved parsing accuracy.

confusion among the roles of concrete vs abstract time and place, and direct vs indirect object relations has been removed.


We find that there is no single representation and method that works equally well for all target domains.

there are large differences between target domains that are more similar to the source domain and those that are less similar.


Omitting case normalization (line 4) consistently hurts for WEB TDs, but helps for BIO.

for BIO it is better not to casenormalize words.


The correlation between discourse structure and perceived influence of participants has also been studied (Ng et al., 1993;Ng et al., 1995).

factors such as frequency of contribution, proportion of turns, and number of successful interruptions have been identified as important indicators of influence (Reid and Ng, 2000).


For example, given "the pink ball near the table", all the elements of an ICG must try to match this description and the context.

if ball01 is pink, but it is on table02, the ICG ball01-location_near-table02 will have a good description match but a bad reality match, while the opposite happens for ICG ball01-location_on-table02.


The first row for each description displays what was spoken, the second row displays what was heard by the ASR, and the third row shows the semantic labels assigned to each segment in the description by a shallow semantic parser (Section 3.2).

in the first example, the preposition "to" is missing, and the object "stool" is mis-heard as "storm"; and in the second example "the plate" is mis-heard as "to play", and the noisy "it" has been inserted by the ASR.


This principle is normally used for model selection, based on the following formulation: which strikes a balance between model complexity and data fit, i.e., the highest-probability model that best explains the data is the best model overall.

the best model is not necessarily the model that fits the data best, as such a model may over-fit the data; the model itself must also have a high prior probability.


In principle, the penalty for replacing a word should depend on both the probability that it is wrong (as for noise removal) and on the similarity between the wrong word and the proposed replacement.

the higher the probability that a word is wrong, and the higher the similarity between the original word and the replacement, the lower the penalty for the replacement.


The first important question we address in this paper is, how can we mine relevant information from online forums and, in essence, crowdsource the creation of contact center DSSs?

we show how many problems faced by consumers can be captured by actions on attributes (e.g.


One obvious benefit of triangulation is to increase the coverage of the model on the input text.

we can reduce the number of out-of-vocabulary words (OOVs), which are a major cause of poor quality translations, using other paths to the target language.


Instead, we resorted to a simpler approach for tuning: each triangulated model is separately tuned against the direct model with a fixed weights (we used a weight of 1).

three ensemble models are created, each on a single triangulated model plus the direct one.


By exploiting such constraints, we can experiment with larger sizes of parallel data.

a more natural experimental setup for triangulation which we would like to try is to use a small direct system with big src pvt and pvt tgt systems.


In this paper we demonstrated that the addition of semantic features can significantly improve transliteration accuracy.

it is possible to outperform the top-1 accuracy of a state-of-the-art phrase-based SMT transliteration baseline through the addition of a bilingual topic model.


The results in the right side of Table 2 show that our MT-aware summarization framework achieves the best results in different ROUGE configurations and outperforms the state-of-the-art summarizer (+1 point ROUGE-1).

our Arabic translated summaries generated using SUMT, are the most fluent and have the most similar structure compared with the Arabic model summaries.


At the end, it returns the final weight W .

the k-best-list is obtained using H as a feature vector with its weight vector W derived from grouped weights W G through unpacking: if h j ∈ g k , then W j =W G k .


Minimization of Eq.2 makes some components in W equal and thus achieves a feature grouping effect.

w i = w j means that h i and h j lie in the same group, i.e.


the subgradient of δ(f, e , e * , W t ) in line 4 is defined via the following equation: The main technique is the proximity operator for group optimization in line 5, which tries to minimize the function Q(•; a, t, λ 1 , λ 2 ) with a = 2 × W t + ∇ W δ(f, e , e * , W t )/t : In the next Section, we will present the details of this proximity for group optimization.


the v value v(g) in line 5 is defined as where p * (i) denotes the inversion of p such that p p * (i) = i.


We evaluate the methods based on precision (P ), coverage (C), as well as the volume (V ) of the extracted NEs.

precision is defined as the percentage of correct NEs of a given class from the automatically extracted ones.


After observing the extraction patterns, we found that the low coverage of songs is mainly due to the complexity of the patterns.

the titles of music websites usually contain not only the song's name, but also the singer's name.


Besides, as pointed out in Section 4.3, title complexity is the main problem that hinders NE extraction.

some titles contain more than one NE, which makes it difficult to induce text-patterns for a certain class from these titles.


1 http://web-ngram.research.microsoft.com/info/ 2 http://www.wikipedia.org/ The objective function is defined as: where d is a tweet from Tweet stream, {s 1 , ..., s m } are the segments in tweet d and C is the function which measures the stickiness of a tweet or segment.

: where the length L(s) is defined in Eq 3, and a longer s makes it typically less sticky.


All these events construct our final event type paradigm.

we carry out experiment on 633 documents from the ACE 05 corpus, 6000 sentences from the Financial News corpus and 6000 sentences from the Musical News corpus, respectively.


In this paper, we aim to tackle the situation when the category of eventtype is undefined, and we try to derive the eventtypes from the corpus.

we automatically build an event type paradigm by using a trigger clustering algorithm: 1) we introduce a trigger extraction algorithm based on the dependency syntactic structure; 2) a trigger filter is then constructed to remove some noisy candidate triggers; 3) we develop an event type discovery algorithm based on our proposed trigger clustering methods.


Conversely, a writer of a truthful review is more likely to make similar comments about some particular aspects of the product as other truthful reviewers.

we postulate that by aligning a the profile of a product and the description of the writer's personal experience, some useful clues can be revealed to help identify possible deception.


Such alignment features could serve as useful signals to differentiate between deception and truthfulness.

we define compatibility for two types of aspects of a product -distinct aspects and general aspects.


In this paper, we analyze political debates where the power differential is dynamic.

we analyze the 2012 Republican presidential primary debates.


In this study, we explore how the power differential between the candidates manifests in these debates.

we use the 20 debates held between May 2011 and February 2012 as part of the 2012 Republican presidential primaries.


We used features to capture the language used in the debates as well as the structure of debates.

we analyze each debate participant in 4 dimensions -what they said (lexical features), how much they spoke (verbosity features), how they argued (argument features), and how they were talked about (mention features).


Building on insights from these studies, we investigated if the modes of addressing candidates change with respect to their power.

we looked at four modes of addressing -FN (First Name), LN (Last Name) FLN (First and Last Name) and TN (Title followed by Name, first, last or full).


We found statistically significant high positive correlation between the power indices of candidates and how often they were referenced/mentioned by others (MP).

as candidates gain more power, they are referenced significantly more by others.


In 5, the scope of the permissive ﻧﺴﻤﺢ nsmH (allow) is the deictic ذﻟﻚ *lk (that) which refers to the clause ﯾﮭﺎن اﻟﻤﺼﺮﯾﻮن ﻟﻦ ln yhAn AlmSrywn (Egyptians won't be humiliated).


For Stage 3, we automatically extracted the triggers that followed our semantic-syntactic working definition of modality.

triggers labeled as synonymous to one of the synsets in Table 3 AND as modifying a clause or a verbal phrase.


We conclude that:  Except for evidential sensory, modality triggers are more likely to modify clauses and verbal phrases than other linguistic constituents such as noun, adjective and adverb phrases.

modality triggers subcategorize for clauses and verbal phrases.


We considered a set of source phrases that are common in both grammars and analyzed their probability distributions over the translation options.

we use the Q-Q plot to study the behaviour of two probability distributions as explained below considering the Chinese phrase 联 合国 (united nations) as a representative example.


Illustrated in Figure 1, it can be observed that all the classes of training corpus are biased.

positive sentences account for the majority class in topic 3, 6, and 11, which is different from the other topics.


The goal of our research is to show that this assumption is incorrect, and that contextual differences can be identified via standard distributional approaches using suitable features.

we aim to provide support for the following hypotheses: • Hypothesis A.


Their distributions may well be similar with respect to certain features (namely the ones commonly used in similarity measures), but our goal is to show that it is possible to identify distributional features that allow an automatic distinction between synonyms and antonyms (Hypothesis A).

we expect synonyms to have a higher level of distributional similarity than antonyms (contrary to Mohammad et al.


Ambiguity is inherent to human language.

word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning.


Next, local and topical features are extracted from the context of the ambiguous word.

we use the current word and its part-of-speech, a local context of three words to the left and right of the ambiguous word, the parts-of-speech of the surrounding words, the verb and noun before and after the ambiguous words, and a global context implemented through sense-specific keywords determined as a list of words occurring at least three times in the contexts defining a certain word sense.


Intuitively, a smoothed language model should enhance the coherence between terms with large semantic association, and analogously for those positional adjacent terms.

the terms that are close to each other (either semantically related or positionally adjacent) should have similar (smoothed) language models; the closer they are, the more similar their smoothed language models are.


To capture the proximity heuristics, we assign "close-by" words with higher propagated counts than those "far away" from the current word.

most propagated counts come from "nearby" words.


The dependency structure can thus be organized as nested word sequences.

a modifier word sequences of a head word is in a form of 1 is a lexical ϕ-word sequence.


Because our model is essentially equivalent to the generative Model C in Eisner (1996), the consistence of the language model can be guaranteed.

⟨O⟩m ϕ 1 ⟨/O⟩ (O ∈ {L, R}) is generated as a Markov sequence to serve as the modifier word sequences (left/right separately) of the head word.


(1) is the probability of the dependency Ngram and thus the parameter of our model, where the dependency relation and valence are modeled uniformly for arbitrary order parameters.

a dependency N-gram can be denoted by an N-tuple of the absolute positions of words in a given sentence.


In order to investigate the fundamental properties of the model and algorithm, we do not use any pruning or approximating methods in the parameter estimation.

we collect from the raw corpora all possible lexical dependency Ngrams 15 without any cut-off thresholds for models of every order.


Because different order dependency N-gram models are trained separately, we use linear interpolation in calculating the test set perplexity.

we use the hand-out development set to tune the interpolation coefficients (weights) and to select the iteration times of different order models to minimize the development set perplexity.


Experiments on Named Entity Recognition task suggest that our proposed method gives consistent improvements over a baseline MaxEnt model and conventional LOP experts.

our method gives the most improvements in recognizing entity types for out-of-vocabulary words.


The proposed automatic expert induction scheme (row elitist LOP) gives consistent and statistically significant improvements over the MaxEnt baselines on both CoNLL and MUC test sets.

on the CoNLL test set, we ob-   serve an improvement of 3.7% absolute F 1 over Maxent-2 .


On the CoNLL dataset, our method gives significant improvements over MaxEnt in both OOV and IV category.

improvement from OOV subset (3.4% in F 1 ) is larger than the improvement from IV subset (2% in F 1 ).


we quantify heterogeneity with regard to the distribution of relevant information and we provide a selfsupervised online adaptation algorithm that learns which pipeline schedule to choose for what input text in order to optimize efficiency while maintaining precision and recall.


Manshadi and Li (2009) and Li (2010) consider the semantic structure of queries.

li (2010) defines the semantic structure of noun-phrase queries as intent heads (attributes) coupled with some number of intent modifiers (attribute values), e.g., the query [alice in wonderland 2010 cast] is comprised of an intent head cast and two intent modifiers alice in wonderland and 2010.


As a countermeasure it could be considered to register some words, which appear only in non-harmful entries, like "splendid", with nonharmful polarity and calculate the relevance of non-harmful entries as well.

firstly, the non-harmful words could be registered in the dictionary.


We use Saurí and Pustejovsky's factuality values except for these changes.

we divide the certainty axis into the values certain (CT), probable (PR) and underspecified (U), and we also divide the polarity axis into positive (+) and negative (−).


However, we find that it is crucial to determine boundaries whether the analyzer should propagate the factuality.

it should resolve the scope of negation and speculation though the actual analyzer regards all embedded contexts as the scope.


As a result, coverage of existing lexical resources is sufficient but issues regarding the semantic ambiguity of functional expressions and issues regarding scope were found.

it was revealed that the problem regarding scope is most significant.


Therefore, we introduce a semantics layer upon the distribution layer to exploit semantic relations.

we adopt an extraction system to populate semantic relation instances, and then derive relation-based similarities from the system's output.


We do not take this approach because such manner suffers from data sparseness.

sometimes the relation words of w and w ′ are quite similar, but none of them are shared by w and w ′ (this means the cosine similarity will be 0).


Fourth, when comparing CWS and POS tagging, we can find the same trend in feature augmentation across different tasks.

when feature augmentation on CWS has higher improvement, usually it also brings higher improvement on POS tagging when comparing across different test data (e.g., the improvement on BC is higher than NW for CWS, and the same is true for POS tagging).


Since we have solved this problem via generalized abbreviation prediction, we hope to apply generalized abbreviation prediction on improving information search.

we apply generalized abbreviation prediction for "query expansion" in Chinese web search.


This paper presents a graph-based model that integrates prosodic features into an unsupervised speech summarization framework without any lexical information.

it builds on previous work using mutually reinforced random walks, in which a two-layer graph structure is used to select the most salient utterances of a conversation.


Given some initial scores F U and F P for utterance and prosody nodes respectively, the update rule is given by: U and F (t) P integrate the initial importance associated with their respective nodes with the score obtained by between-layer propagation at a given iteration t. Hence, the scores in each layer are mutually updated by the scores from the other layer, iteratively.

utterances that exhibit more pronounced signs of important prosodic feature are progressively scored higher.


We focus on Indonesian as our case study, a language that is spoken by more than 165 million speakers, but is nonetheless relatively under-resourced in terms of NLP.

we focus on the syntactic variation that arises with the affixing of the Indonesian suffix-kan, which varies according to the kind of stem it attaches to.


We investigate the viability of automati-cally inducing coarser-grained types that represent morpho-syntactic variation, and we test whether the method we define is suited to such a task.

we focus on a case study detailed in Section 2 on the syntactic and semantic variation that arises with the affixing of the Indonesian suffix -kan.


For our case study, we aim to discover groups of like stems that, when used predicatively in the same morphological context, give rise to the same syntactic behaviour.

we aim to induce classes of stems that exhibit the same syntactico-semantic behaviour when they have the same morphological marking.


Linguists have tried to characterise stems according to their behaviour when affixed withkan (Dardjowidjojo, 1971;Arka, 1993;Vamarasi, 1999).

vamarasi (1999) claims that kan is a good diagnostic for separating unaccusative from unergative stems, which predicts their morphosynatic behaviour.


Stems that are grouped together should exhibit the same semantic shifts.

if affixing -kan to a stem gives rise to a causative meaning, then its corresponding group member will also produce a causative meaning when -kan is applied to the stem.


The output of this step produces topic models.

we perform HAC over raw unigram frequencies and induced topic models from these raw frequencies to ascertain the usefulness of the HDP step.


These two components have different roles.

the core component alone can be used to detect the THEMEs of positive regulation events (e.g., "IFN-induced IP-10"), but the subordinate component alone cannot.


Johansson andNugues, 2008, Miwa et al., 2010a), with few exceptions including Kilicoglu and Berger (2009) and Joshi and Penstein-Rosé (2009).

kilicoglu and Bergler (2009) manually constructed a total of 27 dependency path patterns by examining dependency paths between triggers and arguments.


It means that the performance of the ranked paraphrase candidates depends on the factors we selected and added.

our framework is augmentable and is able to accommodate various factors to optimize the quality of paraphrase extraction.


The optimized parameters show that the lexical and syntactic distributional similarity factors make a substantial contribution to our model.

the words as well as the POS tags appear in both left and right sides show satisfactory performance.


The sentence is taken from a medical abstract, and clearly recommends the use of bronchodilators and inhaled corticosteroids, which are the context interventions in this case.

it has a positive polarity for this task.


We will further evaluate the effectiveness of our clustering algorithm in the ESA (Emergency Situation Awareness) system [Yin et al., 2012] in larger-scale datasets.

we will test its performance together with the burst detection module for identifying significant eventbased clusters from the real-time Twitter stream.


• The first experiment evaluates the individual contribution of different feature types to question classification precision.

we use Weka (Hall et al., 2009) to run machine learning algorithms namely Decision Tree (DT) (Quinlan, 1986), Naive Bayes (NB) (Bayes, 1763), SVM and Voting (Parhami, 1994), which are trained from our data we built using the following feature set: bag-of-words.


As we expected, with both bag-ofwords and keyword features used together, although the precision of classification using DT or NB only increases slightly, it increases significantly if we use SVM.

with 6 coarse-grained classes, the precision increases from 91.2% (table 2) to 94.1% (table 3), and with 50 fine-grained classes, it increases from 83.1% (table 2) to 85.4% using SVM (table 3).


Any type of correspondence that cannot be classified into these types above is marked Paraphrase.


For every word in the word list, exactly one split will be there in the splitset.

splitset  The stemming problem addressed in this paper is defined as follows.


The Minimum Stem Set model (MSS) identifies the correct splitset by minimizing the number of distinct stems.

this model identifies the splitset with the minimum number of distinct stems as the correct split.


Thesauri are useful resources for many NLP applications.

taxonomic thesauri which contain synonymy and hypernymy relations are important for natural language generation (NLG) systems which must make decisions regarding lexical choice and aggregation.


In some cases, the terms themselves can point at the relation among them.

hyper-nyms are sometimes lexical subsets of their hyponyms (String Theory is a hypernym of Super String Theory; Leukemia is a hypernym of lymphocytic leukemia which in turn is a hypernym of B-cell chronic lymphocytic leukemia).


For example, A > B means A follows and depends on B.


We don't deal with festivals as most of them are already lexicalized and are beyond the scope of entities.

they can be recognized with a dictionary in a general word segmentation task.


Semantic class feature: If both RE j and RE i agree in their semantic classes then this feature is set to true, otherwise false.

this feature checks whether the pair either belongs to person class or organization class or location class.


Given the lesser amount of work on event coreference, our understanding of the task is arguably fairly limited.

while it is not surprising that the performance of an event coreference resolver depends heavily on the quality of the output of its preprocessing components, it is not clear to what extent the noise inherent in the output of each preprocessing component is limiting the performance of a resolver.


Our decision to focus on Chinese can be attributed in part to the lack of publicly available results on Chinese event coreference resolution.

to our knowledge, almost all recent work on event coreference has reported results for English (e.g., Humphreys et al.


Second, although we discuss the results in this section in terms of Avg F, it turns out that in these experiments Avg F exhibits the same performance trends as those of MUC, B 3 , and CEAF e .

the significance test results that we obtained using Avg F remain unchanged when Avg F is replaced with any of the three scoring metrics.


bagof-word approach, with user-oriented strategies.

these changes are geared towards improving the precision, contextualization, and personalization of the search results.


This set of categories was deliberately chosen as a means of enriching search results regarding general user interests, and thus aimed at providing a substantially better overall user experience.

named entities are normally utilized for devising the lay-out and the content of the result page of a search engine.


On a final note, as a means of quantifying the impact of the first step on NERQ-2S, we replaced the output given by the first CRF model with the manual binary token/annotations given by the editorial team.

the "new" feature is now a manual input instead of an automatically computed property.


We append one constraint during mining: w h and w n are adjacent.

the phrases mined are all contiguous without gaps.


This brings new challenges for building systems to categorize and understand the sentiments in those reviews.

document-level sentiment classification systems aim to determine either a positive or negative opinion in a given opinionated document (Turney, 2002;Liu, 2010).


In total 10 times of cross fold-validation experiments for this accuracy, the NB accepted 249 documents to perform classification and rejected 1751 documents to forward to the SVM.

the NB correctly classified 236 documents whilst the SVM correctly categorized 1519 documents.


Our study focuses on the semantic categorization of question-like search queries, which cover a wide variety of informational queries that do not necessarily bear named entities.

this paper studies the impact of preceding queries in user sessions for tackling the lack of context in this semantic categorization.


Answers, and hence with one of its categories.

we studied the impact of until nine historical queries.


The study of Statistical Machine Translation (SMT) (Lopez, 2008) relying on syntactic information has received wide attention in recent years.

syntactic information is being integrated either on the source or target or both side(s) in training translation models for handling the translation task.


As syntax information is provided for both sides, for each pair, it must have a node in both trees which subsumes the corresponding string.

non-terminal label checks to their related syntax nodes are necessary in assigning correct tags to all non-terminals in the rules.


As an example, in Chinese tag-set, as verb phrase related tags (VP, VCD, VCP, VNV, VPT, VRD, VSB) are all grouped into CVP, more coverage in the selection of rules is expected.

suppose that in the original tag-set, "想 一 想" (think) is tagged as VCD (verb compounds), while in universal tag-set, it is tagged as CVP.


When Chinese is considered as the source, results are lower than the ones targeted for European languages, probably affected by the corpus selection, size of the corpus, parsing success rate, non-standard linguistic phenomena , etc.

we observed that the parsing accuracy (either on the original or universal tag-set) for Chinese language is lower compared   with other languages, which possibly led to poorer alignment relationships at tree level.


Firstly, we weigh edges according to connecting words' syntactic parts-of-speech via edge multiplier m. We distinguish content words (e.g., nouns, verbs, adjectives and adverbs) from are not and implement three different levels of content-word score aggregation.

we have slightly content word centered score propagation when m>1 in Step (4a) and m=1 in Step (4b) to (4d), while we have moderate content word aggregation when m>1 in Step (4a) and (4d) and m=1 in Step (4b) and (4c).


With the same relations in CN (human) of conductor, we get the similarity of children nodes as one relation similarity of a pair of CN.

the similarity of children nodes which have the same relation with their respective father nodes will be computed.


Knowledge is often acquired from the fundamental analysis.

dependency parsing has been used for some tasks like case frame compilation (Kawahara and Kurohashi, 2006), relation extraction  and paraphrase acquisition .


This paper proposes an example driven approach to improve the quality of MT system outputs.

we extend the system combination method in SMT to combine the examples by two strategies: 1) estimating the confidence of examples by the similarity between source input and the source part of examples; 2) approximating target word posterior probability by the word alignments of the bilingual examples.


Outside the world of cQA, automatic answer ranking might, for instance, be used to prioritize lists of answers found in FAQs or embedded in running text.

we are interested in learning a reranking model that is generally applicable to question answering systems.


Disputed document Q is then compared to each author profile A k using a metric D(Q, A k ) and is attributed to that author for whom D is minimum.

for given query text Q and author profiles A k The distance function D depends on the method used and is defined separately for each method and so is true for the author profile A k .


However, the "document-level" semantic cues are not directly incorporated.

documents are only treated as a sequence of words when determined the similarity between a class and a document (cf.


Given a previously unseen URL U , such a cluster could thus be used as a source of potentially relevant terms for that URL's summary.

these relevant terms include abstract terms, which may not otherwise be observed in the input data.


The content of reference summaries is likely to be only partially relevant to a previously unseen URL.

certain paths in the summary graph may contain nodes whose surface form is target-specific.


Using the summary graph topology and the features defined above we express the abstract Web summarization task as a structured learning problem.

we seek to obtain edge weights such that the cost of individual reference summaries -which, as we saw earlier, are mapped to paths -is minimized.


Text watermarking is a digital watermarking problem where the aim is to embed a secret message (a sequence of bits) in a text in order to make the actual text copy individually identifiable (Bergmair, 2007).

given a natural language source text (e.g.


in our approach a classifier first detects uncertainty cues in a text.


The first approaches to uncertainty detection were based on hand-crafted lexicons (Light et al., 2004;Saurí, 2008).

conText (chapman et al., 2007) used lexicons and regular expressions not only to detect cues, but also to recognize contexts where a cue word does not imply uncertainty.


However, the relative advantage of sequence taggers  In our classification setup, each token is a separate instance, described by a set of features collected from a window of size 2 around the token.

each feature can be present under 5 different names, according to their relative position, except for sentence beginning and ending tokens (out-of-sentence positions are discarded).


Using these results, we conclude that the network predicted by our system that uses tree kernels performs well in terms of extracting an unweighted, undirected network from Alice in Wonderland.

the tree structure derived from the phrase structure tree (PET) performs the best on most of the SNA metrics.


We first need to know the Kana pronunciation of the Kanji character "炒", which is "いた".


However, it is not a trivial work for modelling the context.

how to choice the context from largescale Web pages such that the context is optimized to be used in a mobile device oriented Japanese IME?


Likelihood ratio is an approach to hypothesis testing, which has been proved to be appropriate for sparse data (Manning and Schütze, 1999).

even for candidate phrases will relatively low frequencies, if they share a strong relation with each other, then they are still possibly kept.


We provided the training process of our IME model, n-pos model for local Kana-Kanji conversion and ngram model for online cloud service.

we described an online algorithm of mining new compound words, together with the detailed postfiltering process to prune the billion level entries to be used in mobile services.


The elimination rules eliminate the wrong ones as much as it can.

the selection rules completely disambiguate words, and the elimination rules reduce the ambiguity levels of words.


However, in some words, all parses have same inflections after their last derivations so that they have the same grammatical function in the sentence.


However, instead of scanning all possible sentence segmentations and calculating the phrase-based entropy, we use a simpler method based on the lexical translation table.

the entropy for each lexical entry is calculated as where T is the set of possible translations of word s, and t is a translation.


The communicative tree T c of a sentence c is a set of nodes, with V t = 0, ..., n as a set of terminal nodes and V c = o , 1 , ..., m as a set of non-terminal communicative (label) nodes; E ⊆ V × V is a set of edges; L is the set of communicative labels (in the case of thematicity: SP, T, R and P); δ : E → L is a labeling function for nodes; 0 is the root node.

we interprete the T c as a kind of constituency tree.


The Accuracy Score (AS) measures the correctly assigned thematicity tags on a token basis in the same way as PoS tagging is evaluated.


Results from Table 1 suggest that SLNN does not seem to benefit from the non-linear architecture on either the NER or Syntactic Chunking tasks.

on the CoNLL and MUC dataset, SLNN resulted in a 1% performance drop, which is significant for NER.


CW is known to offer faster convergence than the conventional online learning algorithms such as perceptrons and passive-aggressive methods.

cW makes learned samples hard to forget.


He stated that some interference effects will be observable in the translated text.

a translated text will carry some fingerprints of its source language.


The training and test data are generated in similar ways as in our previous experiment.

80% of the data is randomly extracted for training and the rest of the data is used for testing.


This suggests that Romanian and Bulgarian translators may, due to grammatical convergence of their languages make, given two equivalent structures in any target language, the same (structurally motivated) choices and hence leave a very similar imprint.

sprachbund membership as well as language family could be decisive factors for a classifiers performance.


In this paper, we focus on the task of linking time expressions to events, i.e., we carry out a classification task, where, for each possible pair of (event/relation, time) in a document, the classifier decides whether there exists a link between the two.

we assume that the event mentions, relation mentions and time expressions are given to us by an external process.


We define methods to deal with time-event relations, where the text fragment indicating the time expression, e.g., the appointment took effect Nov. 13 of the example above, is separated from the main event, e.g., succession and stepping down.

our representation is constituted by a pair of shallow syntactic trees (one for each sentence containing the relation arguments), where their nodes are enriched with semantic labels, i.e., EVENT and TIME.


The extraction of relations between entities has been a long-standing topic of research, with work spanning more than a couple of decades, e.g., ACE (Doddington et al., 2004) and MUC (Grishman and Sundheim, 1996).

sentence-level Relation Extraction (RE) has been typically modeled with supervised approaches, using manually annotated data, such as ACE (Kambhatla, 2004).


Structured product data are most important for online shopping malls.

product attributes and their values (PAVs) are crucial for many applications such as faceted navigation and recommendation.


Comprehensive evaluation of each component: Automatically induced KBs, annotation methodology, and the performance of IE models based on the automatically constructed corpora.

the annotation methodology and the IE models are evaluated by using a dataset comprising 1,776 manually annotated product pages gathered from eight product categories.


We built a single model for each category.

we did not build a separate model for each attribute of each category.


We can see that the accuracy of each category is promising.

the accuracy of pairs with MF greater than one is 90% or more.


Model w/o filters: Training models based on corpora naively annotated using KBs.

filters for incorrect and missing annotations are not applied.


In addition, Model with miss only achieved higher performance than Model w/o filters.

the recall of the method improved by 5.2%.


A frame-word interaction feature is a binary feature composed of (1) the name of the frame f from which it is created, and (2) an unordered word pair in which the words are taken from two frame elements of f .

for each pair of frame elements f e 1 and f e 2 of a frame f , we cre-ate one frame-word interaction feature from each unordered word pair composed of one word from f e 1 and one word from f e 2 .


A frame-pair feature is a binary feature composed of a word pair corresponding to the names of two frames, in which the target of the first is present in a frame element of the second.

for each frame element f e of a frame f , if a substring of f e is the target of a frame f 2 , we create a frame-pair feature composed of the ordered pair (f 2 , f ).


Almost all questions in these subjects are annotated as domain-specific inference, indicating that simply referring to knowledge sources does not suffice, and inference engines, such as formula processing modules and ontology-based reasoning, will be required.

nearly half of the questions in Physics are answered in formulas, indicating necessity of formula processing.


Another type of corpora has been developed for evaluating NLP applications, such as machine translation and question answering, which are often provided in application-oriented evaluation campaigns (Voorhees and Buckland, 2012;Kando et al., 2011;Catarci et al., 2012).

the development of language resources is initiated by the demand for NLP tasks/applications.


One weakness shared by virtually all existing coreference evaluation metrics is that they are linguistically agnostic, treating the mentions to be clustered as generic rather than linguistic objects.

while MUC, B 3 , and CEAF were designed for evaluating coreference resolvers, their linguistic agnosticity implies that they can be used to evaluate any clustering task, including those that are not linguistic in nature.


In addition, K(d) and S(d) refer to the set of gold chains and the set of system-generated chains in document d, respectively.


To compute the number of common links, a partition P (S j ) is created for each system chain S j using the key chains.

is formed by intersecting S j with K i .


To see why these weight functions are important, note that any interaction between a scoring metric and a coreference chain is mediated by one of these weight functions.

if these weight functions are linguistically agnostic (i.e., they treat the mentions as generic rather than linguistic objects when assigning weights), the scoring metric that employs them will be linguistically agnostic.


This observation makes it possible for us to design a unified framework for incorporating linguistic awareness into existing coreference scoring metrics.

rather than making different modifications to different scoring metrics to incorporate linguistic awareness, we can simply incorporate linguistic awareness into these three weight functions.


The answer is no: since w sing was created to reward successful identification of singleton clusters, a resolver should be rewarded by w sing only if it correctly identifies a singleton cluster.

w L c returns w sing if all of C i j , K i and S j contain exactly one mention (which implies that the singleton cluster C i j is correctly identified); otherwise, w L c returns 0.


To better understand how we might want to define w L s , recall that in MUC, B 3 , and both versions of CEAF, precision and recall play a symmetric role.

precision is computed by reversing the roles of the key partition K(d) and the system partition S(d) used to compute recall for document d. If we wanted precision and recall to also play a symmetric role in the linguistically aware versions of these scoring metrics, it would be natural to define w L s in the same way as w L k , where E is the set of edges appearing in the maximum spanning tree defined over the mentions in S j .


6 Although S(d) contains only correct links, the precision computed by any scoring metric that employs w L s with the above definition will be less than one simply because it contains suboptimal links.

if a scoring metric employs w L s with the above definition, it will penalize a resolver for choosing suboptimal links twice, once in recall and once in precision.


To avoid penalizing a resolver for the same mistake twice, w L s cannot be defined in the same way as w L k .

7 only spurious links (i.e., links between two non-coreferent mentions), not suboptimal links, should be counted as precision errors.


In this section, we design experiments to better understand our linguistically aware metrics (henceforth LMetrics).

our evaluation is driven by two questions.


Note, however, that there is a difference between LB 3 and B 3 : for an erroneously identified singleton cluster containing mention m, LB 3 sets P (m) to 0 while B 3 sets P (m) to 1.

lB 3 puts a higher penalty on precision given erroneous singleton clusters.


Such differences in responses (b), (c) and (d) are captured by LMetrics(W 4 ) and LMetrics(W 5 ).

the LMetrics scores for response (d) are higher than those for response (c), which in turn are higher than those for response (b).


We propose the use of Bayesian causal networks to perform readability diagnosis.

given a document, the objective of our Bayesian network is to recognize the specific parts of the document that are difficult to read.


The work presented in this paper attempts to evaluate and quantify the use of discourse relations in the context of blog summarization and compare their use to more traditional and factual texts.

we measured the usefulness of 6 discourse relations-namely comparison, contingency, illustration, attribution, topic-opinion, and attributive for the task of text summarization from blogs.


Because of this, techniques developed for formal texts, such as news articles, often do not behave as well when dealing with informal documents.

news articles are more uniform in style and structure; whereas blogs often do not exhibit a stereotypical discourse structure.


(Bosma, 2004;Blair-Goldensohn and McKeown, 2006)), discourse relations have been exploited successfully for multi-document summarization.

(Otterbacher et al., 2002) experimentally showed that discourse relations can improve the coherence of multi-document summaries.


Our fusion dataset is constructed from evaluation data for summarization shared tasks in the Document Understanding Conference (DUC) 1 and the Text Analysis Conference (TAC).

2 we use human-generated annotations produced for the pyramid method (Nenkova et al., 2007) for summarization evaluation to produce a dataset of natural human fusions with quantifiable agreement.


This can be expressed through features on token variables that indicate a priori salience, for which we consider patterns of part-of-speech (POS) tags and dependency arc labels obtained from input parses.

we define indicator features for POS sequences of length up to 2 that surround the token and the POS tag of the token's syntactic governor conjoined with the label.


We show that non-content based features perform well on highly inflectional data.

features reflecting errors in orthography are the most useful, yielding about 89% precision and the same recall.


The partic-ipants expressed their intuitions about the predictions.

they said they looked at the way the essays were written, the overall amount of errors.


This result together with the observations made for the lex type of error suggests that the L1s which are closer related to Czech influence the production of the L2 in a less subtle way than more distant languages.

the use of cognates in the incorrect context or use of incorrect stylistic variants might result in a transfer error in this case (Kroll et al., 2002).


We believe that our results are important for SLA.

the results provide empirical evidence for the different types of errors discussed within the Error Analysis approach.


Inspired by the representation learning of word (Bengio et al., 2003), the proposed FCWM includes an input layer, a projection layer, and a non-linear output layer, as shown in Figure 2 (a).

suppose there is a source language sentence, {x 1 , x 2 , .


Therefore, the proposed FCWM plays the role of the function ϕ parameterized by θ 1 , which maps the context L j of each word into vector V L j as follows: Compared with the FCWM, the proposed CCWM indirectly encodes the context words of each word as a compositional semantic representation to represent the OOV.

the proposed CCWM is a novel variant of the standard convolutional neural network (Collobert et al., 2011), including an input layer, a convolution layer, a pooling layer and a non-linear output layer, as shown in Figure 2 (b).


Compared with the encoder with CAR, the targetside OOV's context words of training processing is different from that of the decoding in which targetside OOV's future context is unknowable.

only the historical n words of y i−1 are used to learn the CAR of .


Especially, the NMT and FCWM or CCWM, which are not isolated from each other, are trained by optimizing their parameters jointly.

the θ 1 or θ 2 and the parameters of NMT are optimized jointly.


For specific tasks, Neural Machine Translation (NMT) model, which is based on the Seq2Seq learning, has achieved excellent translation performance in recent years Bahdanau et al., 2015;Luong et al., 2015;Firat et al., 2016).

the NMT model which is built upon an encoder-decoder framework with attention mechanism (Bahdanau et al., 2015) can also pay attention and its decoder knows which part of the input is relevant for the word that is currently being translated.


Basically, in this paper, we train the model with an encoder-decoder neural network and using dependencies in which the input of the source language is in sequence form and the output of the target language will be generated in a linearized dependency-based tree structure.

instead of predicting only words at each time step, the model trains the network to predict both words and their grammatical dependencies as a dependency tree at each time step.


On the other hand, our proposed model's decoder directly predicts the linearized dependency tree itself in a single neural network in Depth-first preorder order so that the next-word token is generated based on syntactic relations and tree construction itself.

our model is able to learn and produce a tree of words and their dependency relations by itself.


Then we traverse this tree in a Depthfirst pre-order traversal, which is the search tree in which tree is traversed from its left subtree to right subtree recursively until current node is empty, to create output with a linearized tree structure to train the model.

for each rooted subtree, governors and dependency labels of the sentence are predicted first, and their information will be used to predict the next dependent words.


That is, for each rooted subtree, governors and dependency labels of the sentence are predicted first, and their information will be used to predict the next dependent words.

the model can capture the dependency information between label-word and wordword pairs to predict the next tokens.


Our assumption is that after generating subtree, the Seq2Dep model can learn that it should generate the Node-closing token "}" next, instead of a chain of words.


In this work, we proposed a method in which the Seq2Dep NMT model is trained by utilizing syntactic dependencies to provide the model more abundant information.

seq2Dep model learns the potential internal relative connections among tokens and their long term syntactic dependencies to predict the next-word tokens.


One can see that attention loss varies substantially across different POS tags.

we focus on the cases of NOUN and VERB which are the most frequent POS tags in the dataset.


However, Figure 3b shows that the average word prediction loss for verbs is actually smaller compared to the loss for nouns.

although the attention for verbs is substantially more inconsistent with the word alignments than for nouns, the NMT system translates verbs more accurately than nouns on average.


We address the problem of contextual information by considering learner errors.

we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word.


Word Embeddings (E&GWE) E&GWE is a model that combines EWE and GWE.

e&GWe model creates negative examples using an error pattern as in eWe and outputs score and predicts grammaticality as in GWe.


The parameters and settings of the network are the same as in a previous study .

in Bi-LSTM the dimensions of the embedding layer, the first hidden layer, and the second hidden layer are 300, 200, and 50, respectively.


Both GN3Par and LTPar suffer from the inexact search problem.

the approximate beam search can cause the correct tree drops off the beam too soon due to lower scores for earlier actions, and thus return a bad a + that causes the model be updated to bias to wrong structures (Line 8 in Algorithm 1).


This paper investigates the problem of dependency parsing with partially labeled data.

we focus on the realistic scenario where we have a small-scale training dataset with FA and a largescale training dataset with PA. We experiment with three settings for simulating PA and compare several directly-train and complete-then-train approaches with five mainstream parsers, i.e., Biaffine, LLGPar, LGPar, GN3Par and LTPar.


, N } denotes the index of the ground-truth token that appears at r-th position in the original sentence.


As in the previous experimental results, MIPA showed a high correlation.

the noise generated by false positives at the upper left of the scatter plot can be reduced by combining PMI and distributional similarity.


However, distributional similarity-based methods have correct paraphrases of higher rank.

mIPA was able to include 10 words of correct paraphrases in the top 20 words; that is, our method can obtain paraphrases with high coverage by using only the highly ranked paraphrases.


MIPA achieved the highest performance on three out of the five datasets.

the proposed method extracted paraphrase pairs useful for calculating sentence similarity at the top-rank.


class, 0.42 for neutral, and 0.78 for contradiction.

on average, four of the five crowdsourced judgments agree with the final label for the entailment and contradiction items, whereas for the neutral items, only an average of two of the five original annotators assigned the neutral label, and the other three were split between contradiction and entailment.


These results indicate that MPE cannot be trivially reduced to SPE.

even if a model had access to the correct SPE label for each individual premise (an unrealistic assumption), it would require more than simple voting heuristics to obtain the correct MPE label from these pairwise labels.


Lin Similarity generates errors as its symmetric structure cannot tell the difference between w l → w r and w r → w l .

it makes errors on non-symmetric examples, like buy → take.


Second, for a general model, transitivities inter-or intra-layers both help it become stronger; however, for a strong model, only the transitivities intra-or inter the observed layer, i.e., involving the gold labels, contribute to the performance gain.

only solid knowledge can make a strong model even stronger through transitivities.


In this paper, we propose a method that exploits an existing phrase-based translation model to compute the phrase-based decoding cost for a given NMT translation.

1 we force a phrase-based translation system to take in the source sentence and generate an NMT translation.


Algorithm 2 shows the forced decoding algorithm that integrates the new rules.

because the translation E is given for the forced decoding algorithm, the proposed forced decoding algorithm keeps I stacks, where I is the length of E. the stack size is corresponding to the target word size during forced decoding while the stack size is corresponding to the source word size during standard phrase-based decoding.


(2011) but vary in the network architecture.

feed-forward neural network (Zheng et al., 2013), tensor neural network (Pei et al., 2014), recursive neural network (Chen et al., 2015a), long-short term memory (LSTM) (Chen et al., 2015b), as well as the combination of LSTM and recursive neural network (Xu and Sun, 2016) have been used to derive contextual representations from input character sequences, which are then fed to a prediction layer.


The Information Bottleneck (IB) method has been successfully applied to clustering in the NLP domain (Slonim and Tishby, 2000).

iB attempts to balance the trade-off between accuracy and compression (or complexity) while clustering the target variable, given a joint probability distribution between the target variable and an ob-served relevant variable.


Furthermore, the IB method has been widely studied for multiple machine learning tasks, including but not limited to, speech diarization (Vijayasenan et al., 2009), image segmentation (Bardera et al., 2009), image clustering (Gordon et al., 2003), and visualization (Kamimura, 2010).

similar to this paper, image segmentation has considered segmentation as the compres-sion part of the IB based method.


Here, C is a chat text sequence, following the notation introduced in the previous section.

the IB objective can be achieved by maximizing the following: the above IB objective function attempts to balance a trade-off between the most informative segmentation of R and the most compact representation of C; where β is a constant parameter to control the relative importance.


The second hypothesis is that it will be useful to learn co-occurrence patterns between properties.

we hypothesize that in learning an entity is a mammal, we may also infer it is four-legged (Hypothesis H2).


Here, the concept representation c Mult is a multinomial distribution over the properties in Q.

(all the properties compete in this model.)


We use an extension of LDA (Blei et al., 2003) to implement our hypotheses on the usefulness of overarching structure, both commonalities in selectional constraints across predicates, and cooccurrence of properties across concepts.


In order to help answering this question, we propose a polymodal word representation based on the theory that humans tend to use diverse sources in developing a word meaning.

we construct six modules for polymodality including linear context, syntactic context, visual perception, cognition, emotion, and sentiments based on the human cognitive model proposed by Maruish and Moses (2013).


We train lexical-relation-specific word embedding using retro-fitting .

let V = {w 1 , ..., w n } be a vocabulary and Ω be an ontology that encodes semantic relations between words in V .


We jointly train the embeddings of images and sentences together into the multi-modal vector space to build vision-specific word embeddings (Kiros et al., 2014b).

let T be the training dataset where one image I i is associated with a corresponding caption sentence S i , i.e., (I i , S i ) ∈ T .


Concatenation (CONC) is used as the first step for ensembling multiple vectors of different dimensions.

let S be a set of n semantic spaces and s i be a single vector space in S. e id ∈ s i is a representation of word w d in the semantic space s i ∈ S. Then the resulting concatenated embedding e d of word w d is where ⊕ is the concatenation operator and i α i = 1.


For f ∈ F and c ∈ C, c ∈ C f if and only if c has the feature f where C f ⊂ C. The valid feature set F v is a subset of F such that f ∈ F v only if there exist more than three concepts that have f , or equivalently, |C f | > 3.

then the to examine how well each representation captures the normalized feature f i ∈ F v , we calculate the cosine similarity between R(c) for c ∈ C f i and R(C f i ) where R(•) is a mapping from concept to its distributed representation and C f i is a centroid of all concepts in C f i or C f i is a centroid of concepts that share the feature f i .


We define the feature density as the cosine similarity between the concept and the centroid.

we calculate the feature density of all target concept-feature pairs assuming vectors that share  Table 3: Spearman's correlation between the CSLB normalized feature representation and the target distributed representation.


The results in Figure 3 illustrate the different preferences among different word groups, which show the distinct nature between the two groups.

the gradual patterns revealed by increasing relative weights of perception and emotion are contrary to concrete and abstract words.


In this paper, we propose a novel approach to capture word sense relatedness in multi-prototype word embedding model.

we differentiate the original sense and extended senses of a word by introducing their global occurrence information and model their relatedness through the local textual context information.


To enhance the expression ability of the embedding model, recent research has a rising enthusiasm for representing words at sense level.

an individual word is represented as multiple vectors, where each vector corresponds to one of its meanings.


Since the fixed replacement lacks of flexibility, the GPU model conducts the reinforcement process following another distribution over the colors.

when a ball of color i is drawn, another A ij balls of color j will be put back.


The original meaning is sampled firstly, and then the extended meanings are sampled through the reinforcement.

we sample an extended meaning according to a conditional probability given the original meaning.


The hyper-parameter 0 ≤ e ≤ 1 is used to control the strength of the GPU reinforcement as well as the number of the extended meanings.

if we set e = 0, the second sample for the extended meanings has been turned off, and then FCSE-2 degrades to the SG+ model in (Li and Jurafsky, 2015), which is another state-of-theart method for multi-prototype word embedding model based on hard clustering.


Compared to full corpus, we can see more apparent gap between NP-MSSG and FCSE-1 (from 2.6% to 3.1%), SG+ and FCSE-2 (from 0.1% to 1.9%).

the gap between FCSE and other methods gets closer when there are adequate training corpus, which is in accordance with the intuition.


Two non-parametric methods for fuzzy clustering produce flexible number of word senses.

fCSE-2 introduces the Generalized Polya Urn process to integrate both the global occurrence information and local textual context information.


Therefore, we first explore the sensitivity of CNNs for sentence classification with respect to the input representations used.

we replaced word2vec with GloVe representations.


As mentioned previously, all the graph embedding methods of the previous section exploit the semantic similarity between words but for an entry, this similarity is not linearly correlated with the rank of its relevant neighbors in the thesaurus.

the relevance of the semantic neighbors of an entry strongly decreases as their rank increases and the first neighbors are particularly important.


As a second way of evaluating the quality of our automatic labeling, we use these silver labels in a semi-oracle trial where we apply the actual simplification operations as given in the annotated corpus.

we simply take the automatic labels as true and use the A  D M R RM RW RWM  C  O   A  240 0  0 0  0  2  0  0  19  D  15 333 8 4  5  1  1  4  0  M  0  1 22 0  0  0  0  1  0  R  0  33 0 28 6  0  0  4  0  RM  0  8  0 0  4  0  0  0  0  RW  3  31 4 7  2  4  0  6  0  RWM 0  6  0 0  0  0  0  0  0  C  0  24 98 1  1  1  0  1807  0  O 105 0 0 0 0 9 0 0 1998 Table 4: Confusion matrix of true (rows) and automatically annotated (columns) operations on the manually annotated data.


Table 5 shows that the LSTM model does not predict the silver labels very well.

the model is relatively conservative with respect to the prediction of simplification operations, and tends to overpredict the majority class (i.e., to copy a token).


In this context, given an RDF data set comprising a collection of entities, where each entity is described by a set of its properties (i.e., all triples with the entity as the subject), our goal is to choose top-k representative triples for each entity.

since all triples associated with an entity (as its description) share the same subject, our objective is to select top-k predicates and their corresponding objects among these triples that best summarize the entity's description.


Since an RDF graph is usually represented as a set of triples, where each triple t consists of a subject s, predicate p, and an object o, in the form of <s, p, o>, we can consider a collection of such triples as a "document".

definition 2 (document): A document d is defined as a set of triples, that describe a single entity e. all triples of a document d have the same subject.


The third model proposed by us is Leaf-Composition-Tree LSTM (LCT-LSTM).Different from the LT-LSTM and TC-LSTM, this model not only takes the composition nodes into consideration when building the LSTM layer, but also makes sum of the outputs of two modules mentioned above.

lCT-lSTM can be seen as the composition of TC-lSTM and lT-lSTM for the reason of not only building the sequential feature for the composition nodes which contain the structural information, but also combining the sequential feature and the structural feature of the input sentence.


In follow-up work, Habernal and Gurevych (2016a) examined the reasoning behind the annotations in their original corpus.

why arguments were selected as more convincing.


In terms of heuristics, we examine the effectiveness of Metric Entropy (ME) of text to predict convincingness, which is inspired by the notion that written English language is well-formed, as opposed to random.


In terms of supervised techniques, we first follow previous approaches to classifying paired data that create separate learned representations of elements in a pair that are then concatenated for the final predictive model (Bowman et al., 2015;Mueller and Thyagarajan, 2016;Potash et al., 2016b).

we experiment with creating separate representations using either a BLSTM or summing individual token embeddings.


For example, if the content of a document is about terrorist attack, the document is more likely to contain Injure events, Die events, Attack events, and is unlikely to describe Be-Born events.

there is a strong association between events appearing in a document.


Notably for Turkish, adding linguistic features and using 100% automatically generated noisy training data, our approach achieves the same performance as the baseline model using 75% manually created clean data and 25% automatically created noisy data.

explicit linguistic knowledge has significantly saved annotation cost (2,367 sentences).


In this paper, we introduce the task of identifying usage expressions from consumer product reviews.

we focus on classifying review sentences as to whether they contain a usage expression or not.


Product reviews often contain usage information.

in addition to opinions on product quality, reviewers often share how, where, or why they use the product.


The TOTAL is the total duration the gaze rests within the area of interest.


Because in order to generate the correct target sequence, the decoder solely depends only on the last hidden state of the encoder.

the network needs to compress all of the information contained in the source sequence into a single fixed-length vector.


This global property allows the decoder to address any parts of the source sequence at each step of the output generation and provides advantages in some cases like machine translation tasks.

when the source and the target languages have different sentence structures and the last part of the target sequence may depend on the first part of the source sequence.


(2015) and Press and Wolf (2016), we do not allow successive minibatches to sequentially traverse the dataset.

we follow the standard practice to reinitialize the hidden state of the network at the beginning of each mini-batch, by setting it to all zeros.


None of the attention mechanisms worked as a proper attention mechanism.

none of the mechanisms generated larger weights for specific words in the sentence, in comparison to the other words in the same sentence.


In this paper, we combine two existing approaches to the problem: (1) the synchronic approach that focuses on interdependencies between features and (2) the diachronic approach that exploits phylogeneticallyand/or spatially-related languages.

we propose a Bayesian model that (1) represents each language as a sequence of binary latent parameters encoding inter-feature dependencies and (2) relates a language's parameters to those of its phylogenetic and spatial neighbors.


In Visual Question Answering (VQA) (Antol et al., 2015), a system is tasked with answering a question about a given image, where the questions are constrained to be answerable directly from the image.

the VQA task primarily serves to evaluate the extent to which the system has recognized the explicit content of the image.


In this work, we explore the idea of incorporating syntactic parse tree into neural networks.

we employ the Tree-LSTM model and Tree-GRU model, which are based on the tree structure, to encode the arguments in a relation.


The only difference between Tree-GRU and Tree-LSTM is how they modulate the flow of information inside the unit.

tree-GRU unit removes the separate memory cell and only uses two gates to simulate the reset and update procedure in information gathering.


We opt to use offline alignment to show that such a low-cost approach does, in fact, capture a significant part of the relationship between words of different languages when it comes to sentiment.

a single transformation (linear in the case of our work) is sufficient to learn a projection which allows one to use labeled English data to aid in sentiment analysis.


Given an input sentence, we first obtain its word representations.

we train two separate word embedding sets for the bottom level syntactic model and top level sentiment model, respectively, denoted as we b and we t , respectively.


In this model, we use a dependency parser to replace the POS-tagging model in Section 2.2.1.

the model of Dozat and Manning (2016) is used, which fuses several BiLSTM layers to encode the input sentence before doing Figure 4: Dependency parsing model bi-affine attention to learn dependency arcs betwtween different words.


For computing pragmatic significance score for each aspect, we employ graph centrality measures.

we propose to use centrality metrics such as closeness centrality, betweenness centrality and page rank, since these centrality measures captures significance of a node from different perspectives.


Syntactic contexts are derived from produced dependency parse-trees.

the bag-of-words in the SKIP-GRAM model yield broad topical similarities, while the dependencybased contexts yield more functional similarities of a cohyponym nature.


Analogously to the traditional threshold technique for maximizing the F1 score in binary classification, we introduce a simple percentage noise to the decoding process.

during the construction of the score table ξ (Equation 7), a label-specific percentage bias is added to the calculation as in Equation 10.


F&H14 hypothesize that discourse features beyond the sentence level also help authorship attribution.

they propose an author has a particular style for representing entities across a discourse.


Hence, we explore a different idea, addressing lightlysupervised argument persuasiveness scoring via an error-modeling approach.

guided by theoretical work on persuasiveness, we begin by defining a set of errors that could negatively impact an argument's persuasiveness.


In contrast, the debate comments in our corpus are written in response to a given debate topic.

each comment is written independently of the other comments and therefore can be understood without them.


We ask two native speakers of English to annotate each of the 1,208 arguments with a persuasiveness score after familiarizing them with the (topicand domain-independent) scoring rubric (see Table 2).

we ask our annotators to score each argument's persuasiveness on a scale of 1−6.


These rules make sense because a justification that discusses its assertion's topic's effects frequently is likely to be very topically coherent, thus having a clear justification.

recall that for each error type t, our heuristics only label a subset of the training arguments with error severity values for t. 11 To label the remain- 11 The heuristics for GE, LO, IS, UA, and UJ can label 39%, 66.%, 86%, 50%, and 87% of the training arguments, ing training arguments, we apply EM to bootstrap from the heuristically labeled seeds for t. we initialize the model parameters using only the seeds for t. After that, we iterate the E-step and the M-step until convergence.


To fully specify the model parameters, we need to specify the features used to represent each argument.

we employ the 10 features used in the heuristics described in the previous subsection.


Regardless of which error type we train the model for, the same set of 10 k-valued features will be used.

the generative models for the five error classes differ only w.r.t.


Like many other unsupervised and weaklysupervised models, we make a modeling assumption in our approach in order to facilitate learning in an environment where annotated data is scarce.

we assume that the persuasiveness score of an argument inversely correlates with the sum of its severity scores over all errors.


Style Our fourth baseline captures aspects of an argument's style.

it employs four types of features that are motivated by Tan et al.


The dataset covers the six-month period beginning January 1, 2012, and was limited to conversations where the responders had engaged in at least 60 3turn conversational Twitter interactions during the period.

these are people who reasonably frequently engaged in conversation, and might be experienced "conversationalists."


This issue was addressed by ad-justing the class weights in order to stronger penalize false positives when training the random forest model.

the class weight of the negative class was set to be num non replies num replies while the class of the positive class was 1.


Here, we name our proposed model the cascading multiway attention (CMA) model.

each sentence is first represented with multiple representation vectors through using different ways of attention to focus on the important part.


On the document level, we keep generating multiple kinds of attention to sentences and get the document representation, which is fed into a softmax function for classification.


Here, we use a non-linear layer to project d into the space of the targeted C classes.

where W l and b l are the weight matrix and bias respectively.


Thus, we propose the multiway attention mechanism to capture sentence focuses with repsect to different views.

in our model, we represent each user and each product with a representation vector which is learned from the model, notated with u and p here.


As CNN and LSTM sentiment vectors are, however, generated from models of sentiment classification, these vectors are easily separated in terms of sentimental categories by machine learning methods.

a multi-layer NN sentiment classifier using both of these vectors as input reaches the state of perfect classification on the training set after a few epochs.


In this paper, we aim to propose a domain adaptation method for document-level sentiment clas-sification based on our earlier joint model (Yu and Jiang, 2016).

instead of predicting the occurrence of pivot words as in previous work (Blitzer et al., 2007;Yu and Jiang, 2016), we introduce a new auxiliary task based on sentiment scores of pivot words.


To reduce the reliance on external resources, we also experiment with another method based on mutual information (MI) on the source labeled data D s to automatically derive a pseudo sentiment lexicon.

we first extract only adjectives, adverbs and verbs from the documents in D s , and measure each remaining word's MI with the positive and the negative classes: where w i denotes the i th word in V, y ∈ {+, −} is a sentiment label, andp(w i , y) is the empirical probability of observing w i and y together.


Finally, to explore how our proposed models help to improve the performance of HNN in the test data set, we conduct a case study on B2D to get a deeper insight of our model DSR.

we sample several samples from the test data set, i.e., DVD.


Recently, deep neural networks start to show promising results in relation extraction.

convolutional Neural Networks (Zeng et al., 2014a;dos Santos et al., 2015;Nguyen and Grishman, 2015), Reccurrent/Recursive Neural Networks such as bidirectional LSTMs (Zhang et al., 2015), LSTM along shortest dependency paths (Xu et al., 2015), bidirectional tree-structured LSTM-RNNs (Miwa and Bansal, 2016) are shown to be effective.


This results in an explicit correspondence between blocks of neurons and relation types.

we partition the neurons in each sub-layer (i.e., LC a , LC b and LC s ) into disjoint subsets and associate each subset with one specific relation label.


The bilingual-Joint-IRL method achieves the best result: it encourages the learnt representation to be discriminative among relation types.

the learnt cross-lingual representation is encouraged to differentiate relation types regardless of language.